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홈뉴스연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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소개
한국어
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  1. 홈
  2. SOTA
  3. 프롬프트 엔지니어링
  4. Prompt Engineering On Oxford Iiit Pet Dataset

Prompt Engineering On Oxford Iiit Pet Dataset

평가 지표

Harmonic mean

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Harmonic mean
Paper TitleRepository
CLIP94.12Learning Transferable Visual Models From Natural Language Supervision
HPT++96.91HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
RPO96.05Read-only Prompt Optimization for Vision-Language Few-shot Learning
MaPLe96.58MaPLe: Multi-modal Prompt Learning
DePT96.37DePT: Decoupled Prompt Tuning
HPT96.71Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
ProMetaR96.49Prompt Learning via Meta-Regularization
MetaPrompt96.26Learning Domain Invariant Prompt for Vision-Language Models
PromptSRC96.30Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoPrompt96.87Consistency-guided Prompt Learning for Vision-Language Models
MMRL96.74MMRL: Multi-Modal Representation Learning for Vision-Language Models
CoCoOp96.43Conditional Prompt Learning for Vision-Language Models
PromptKD97.15PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
0 of 13 row(s) selected.
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한국어

소개

회사 소개데이터셋 도움말

제품

뉴스튜토리얼데이터셋백과사전

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